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4th International Conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Zemmouri, Mohamed Laalam, Fatma Zohra Kazar, Okba Himeur, Yassine Oulefki, Adel Toumi, Chahrazad Mansoor, Wathiq Attala, Shadi Artificial intelligence and Information Technologies Laboratory Faculty of New Information and Communication Technologies Kasdi Merbah Ouargla University Ouargla30000 Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Mohamed Khider University Department of Computer Science LINFI Laboratory Biskra07000 Algeria United Arab Emirate University Department of Information Systems and Security College of Information Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350369694

The integration of big data with cutting-edge technologies like IoT and Cloud Computing has profoundly influenced various aspects of modern life, including everyday service processes. To facilitate data-driven decision-making, big data analytics—focused on identifying patterns, trends, and correlations in large data sets—is indispensable. While traditional statistical techniques are useful, new tools and infrastructures such as Hadoop, Spark, and NoSQL are essential to tackle big data challenges. However, modifying the existing environment can be impractical, especially in production settings, due to the need for significant investment and specialized expertise. This article presents a novel computational paradigm that adds a decision-making layer atop existing systems for data analysis, eliminating the need to alter the environment. The approach treats the current information system as a data lake and introduces a new data recovery layer through web services, drawing inspiration from big data technologies like MapReduce. This system offers the advantage of being modular, reusable, and universally compatible, making it an independent decisional framework that can work with any information system or data source. © 2023 IEEE.

关键词： Big data

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Statistical Security-QoS Provisioning for mURLLC Over Massive-MIMO Based 6G Mobile Wireless Networks Using FBC

Statistical Security-QoS Provisioning for mURLLC Over Massiv...

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MILCOM, Military Communications Conference

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

The 6G mobile wireless networks are expected to support massive ultra-reliable and low-latency communications (mURLLC) with stringent requirements on transmission-delay and error-rate bounded quality-of-service (QoS). Correspondingly, the finite blocklength coding (FBC) has developed as the effective technique to implement small-packet communications for the statistical QoS provisioning over mURLLC-based 6G mobile networks. On the other hand, guaranteeing the security for 6G networks services imposes another new dimension of statistical QoS requirements. However, how to efficiently upper-bound the statistical delay and the error probability over time-varying wireless fading channels while guaranteeing the security QoS using FBC remain a challenging problem. To overcome these difficulties, in this paper we propose and develop the new concept of secrecy ϵ-effective capacity as a statistical security QoS scheme to guarantee the performance of statistical delay-bounded QoS, error-rate bounded QoS, and the secrecy-information leak-probability bounded QoS by using the FBC technique. First, we establish the wireless channel model of massive multiple-input-multiple-output (MIMO) for supporting the communications between the base station and multiple mobile users with the existence of an eavesdropper. Second, we define and derive the eavesdropping rate and secrecy rate under the massive MIMO communication. Third, we derive the secrecy-information leak probability and integrate it with statistical delay and error-rate bounded QoS to define the new wireless network communications performance metrics of secrecy ϵ-effective capacity. Finally, we use the numerical analyses to validate and evaluate our developed schemes for supporting statistical delay and error-rate bounded QoS provisioning while upper-bounding the secrecy-information leak probability over 6G mobile networks using massive-MIMO/FBC.

关键词： 6G mobile communication Military communication Measurement Numerical analysis Wireless networks Quality of service Delays Security Communication system security Capacity planning

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TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System 25

TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Lear...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Jinsong Mao, Xiaoxuan Li, Lu Wu, Hanqian Joint Graduate School Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration of Ministry of Education Nanjing China

ISBN: (纸本)9798350330014

In today's evolving society, the increasing complexity and frequency of meetings necessitate advanced scheduling systems. Traditional methods are constrained by rigid prede-fined strategies, lack intelligent negotiation mechanisms, and often compromise user privacy. Addressing these challenges, we introduce the Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System (TPMARL-MSS). Unlike conventional systems, TPMARL-MSS autonomously learns and refines its strategies through continuous feedback. It features automated negotiation and adaptive decision-making, offering a more nuanced scheduling approach. Importantly, our Thought-Perception module protect privacy, allowing the agent to deduce preferences from participant behavior without revealing personal data. Evaluations on the real-world dataset shows that TPMARL-MSS surpasses traditional methods in efficiency and schedule quality, highlighting its practical applicability. © 2023 IEEE.

关键词： Decision making

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A Transformer-Based Language Model for Sentiment Classification and Cross-Linguistic Generalization: Empowering Low-Resource African Languages

A Transformer-Based Language Model for Sentiment Classificat...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Nilanjana Raychawdhary Nathaniel Hughes Sutanu Bhattacharya Gerry Dozier Cheryl D. Seals Computer Science & Software Engineering Auburn University Auburn AL USA Computer Science & Computer Information Systems Auburn University at Montgomery Montgomery AL USA

Sentiment analysis is a widely studied area of natural language processing (NLP), however, a lot of attention has been given to languages with higher resources, like English. By concentrating on low-resource African languages including Hausa, Nigerian Pidgin, Igbo, Yoruba, Swahili, and Kinyarwanda for which there is a lack of curated datasets for sophisticated AI applications. This research intends to close the gap by examining the viability of current NLP strategies and creating new ones to meet this challenge. Sentiment analysis enables us to comprehend the feelings and viewpoints expressed in textual information. A transformer-based language model is presented in this research for low-resource African languages in order to classify sentiment and generalize across languages. We fine-tune the XLM-RoBERTa model on Hausa, Nigerian Pidgin, Igbo, and Yoruba, and then assess its performance in these languages as well as Swahili and Kinyarwanda, which the model had not previously seen. Our findings show that, even in languages with few linguistic resources, the model is effective at performing sentiment analysis tasks for known and unknown languages. This research helps close the digital language gap by extending natural language processing capabilities to under-resourced African languages.

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DDoS Detection Using Active and Idle Features of Revised CICFlowMeter and Statistical Approaches 4

DDoS Detection Using Active and Idle Features of Revised CIC...

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4th International Conference on Advanced Science and engineering, ICOASE 2022

作者： Ali, Basheer Husham Sulaiman, Nasri Al-Haddad, S.A.R. Atan, Rodziah Hassan, Siti Lailatul Mohd Department of Electrical and Electronic Engineering Serdang Malaysia Faculty of Engineering Department of Electrical and Electronic Engineering Serdang Malaysia Department of Computer and Communication Systems Engineering Serdang Malaysia Software Eng. and Information Systems Dept Serdang Malaysia School of Electrical Engineering Universiti Teknologi Mara Shah Alam Malaysia

ISBN: (纸本)9781665472227

Distributed Denial of services (DDoS) attack is one of the most dangerous attacks that targeted servers. The main consequence of this attack is to prevent users from getting their legitimate services by bringing down targeted victim. CICFlowMeter tool generates bi-directional flows from packets. Each flow generates 83 of different features. The research focuses on 8 features which are active min (f1), active mean (f2), active max (f3), active std (f4), idle min (f5), idle mean (f6), idle max (f7), and idle std (f8). CICFlowMeter tool has several problems that affected on the detection accuracy of DDoS attacks. The idle and active based feature of Shannon entropy and sequential probability ratio test (SE-SPRT) approach was implemented in this research. The problems of original CICFlowMeter were presented, and the differences between original and revised version of CICFlowMeter tool were explored. The DARPA database and confusion matrix were used to evaluate the detection technique and present the comparison between two versions of CICFlowMeter. The detection method detected neptune and smurf attacks and had higher accuracy, f1-score, sensitivity, specificity, and precision when revised version of CICFlowMeter used to generate flows. However, the detection method failed to detect neptune attack and had higher miss-rate, lower accuracy, lower f1-score, and lower specificity, and lower precision when original version used in generating flows. © 2022 IEEE.

关键词： Denial-of-service attack

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Federated Learning Based Integrated Sensing, Communications, and Powering Over 6G Massive-MIMO Mobile Networks

Federated Learning Based Integrated Sensing, Communications,...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

To sense mobile users' positions and channel states while simultaneously conveying information for supporting the 6G traffic, the integrated sensing and communication (ISAC) has been proposed as a key technique for sensing and communicating using the same radio frequency and hardware. Moreover, to sup-port energy-constrained and battery-limited wireless networks, simultaneous wireless information and power transfer (SWIPT) has also emerged to simultaneously deliver information and energy to a receiver. To integrate these two techniques, integrated sensing, communications, and powering (ISACP) has been developed to simultaneously sense the targeted mobile users, transmit the information, and deliver the power. However, how to efficiently optimize the power allocation in ISACP schemes to support the 6G traffic has imposed many new challenges not encountered before. To overcome these challenges, in this paper we propose a federated-learning (FL) enabled ISACP scheme to maximize the energy-efficiency over 6G massive MIMO wireless networks. First, we establish the system models for our proposed ISACP and FL schemes. Second, we design the training data set collection in the FL algorithm by sending the pilot signal from mobile users to the massive MIMO base station, and formulate an energy-efficiency maximization problem for the developed ISACP scheme. Third, To solve the formulated energy-efficiency maxi-mization problem, we propose an actorcritic enabled multi-agent based FL mechanism. Finally, we use the numerical analyses to validate and evaluate our developed schemes.

关键词： 6G mobile communication Wireless sensor networks Federated learning Wireless networks Training data Massive MIMO Integrated sensing and communication

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Integrated Sensing, Communications, and Powering for Statistical-QoS Provisioning Over Next-Generation Massive-MIMO Mobile Networks

Integrated Sensing, Communications, and Powering for Statist...

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Annual Conference on information Sciences and systems (CISS)

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

The integrated sensing and communication (ISAC) has been proposed as a key technique to support the next generation traffic with stringent quality-of-service (QoS) requirements through sensing and communicating using the same radio frequency and hardware. Simultaneous wireless information and power transfer (SWIPT) has also emerged to simultaneously deliver information and energy to a receiver. However, how to integrate ISAC with SWIPT to support the stringent QoS traffic has imposed many new challenges not encountered before. To overcome these challenges, in this paper we propose an integrated sensing, communications, and powering (ISACP) technique for supporting both sensing and communication QoS provisioning while delivering the power over the next-generation wireless networks using massive multiple-input and multiple-output (massive MIMO) communications. First, we develop the system models for the ISACP scheme to simultaneously sense the targeted mobile users (MUs), transmit the information, and deliver the power. Second, we propose a hypothesis testing based scheme to estimate the MU’s angle-of-arrival using the sensing signal. Third, we employ the Cramér-Rao bound (CRB) to measure the performance of sensing and maximize the energy-efficiency with satisfying requirements of both sensing and communication performances. Finally, we use numerical analyses to validate and evaluate our proposed ISACP scheme.

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ISAC-Driven Channel-State-Estimation for Statistical-QoS Based MURLLC Over 6G M-MIMO Mobile Networks Using FBC

ISAC-Driven Channel-State-Estimation for Statistical-QoS Bas...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xi Zhang Qixuan Zhu Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The massive ultra-reliable and low-latency communications (mURLLC) services are emerging as a new traffic type to support communications among massive numbers of mobile users (MUs) demanding the stringent delay and error-rate bounded quality-of-services (QoS) requirements in 6G. Correspondingly, the integrated sensing and communications (ISAC) technique can improve various QoS performances through spectrum/hardware sharing between target-sensing and data-communications. In addition, the massive multiple-input multiple-output (massive MIMO) and finite blocklength coding (FBC) have been also widely recognized as the powerful 6G techniques to efficiently support mURLLC. However, how to apply the ISAC technique to accurately sense and estimate the wireless channel state while transmitting the information from the base station to MUs to support mURLLC using massive MIMO and FBC has imposed many new difficulties not encountered before. To conquer these challenges, in this paper we propose the new ISAC performance metrics for statistical-QoS guaranteed mURLLC over 6G massive MIMO mobile networks using FBC. First, we establish system models for ISAC-based architectures. Second, we detect targeted MUs and estimate the channel state using the radar sensing echo. Third, we develop a new ISAC performance metric as a function of the data-communication rate and channel state estimation distortion. Finally, we use the numerical analyses to validate and evaluate our developed ISAC performances in supporting mURLLC-oriented QoS requirements.

关键词： 6G mobile communication Measurement Wireless sensor networks Wireless networks Channel estimation Quality of service Massive MIMO Integrated sensing and communication Distortion Testing

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Robust Cluster-Based Routing Protocol for IoT-Assisted Smart Devices in WSN

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computers, Materials & Continua 2021年第6期67卷 3505-3521页

作者： Maryam Shaq Humaira Ashraf Ata Ullah Mehedi Masud Muhammad Azeem N.Z.Jhanjhi Mamoona Humayun Department of Computer Science and Software Engineering International Islamic University IslamabadIslamabadPakistan Department of Computer Science National University of Modern LanguagesIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia Department of Information Systems College of Computer and Information ScienceJouf UniversityAl-JoufSaudi Arabia

The Internet of Things(IoT)is gaining attention because of its broad applicability,especially by integrating smart devices for massive communication during sensing ***-assisted Wireless Sensor Networks(WSN)are suitable for various applications like industrial monitoring,agriculture,and *** this regard,routing is challenging to nd an efcient path using smart devices for transmitting the packets towards big data repositories while ensuring efcient energy *** paper presents the Robust Cluster Based Routing Protocol(RCBRP)to identify the routing paths where less energy is consumed to enhances the network *** scheme is presented in six phases to explore ow and *** propose the two algorithms:(i)energy-efcient clustering and routing algorithm and (ii)distance and energy consumption calculation *** scheme consumes less energy and balances the load by clustering the smart *** work is validated through extensive simulation using *** elucidate the dominance of the proposed scheme is compared to counterparts in terms of energy consumption,the number of packets received at BS and the number of active and dead *** the future,we shall consider edge computing to analyze the performance of robust clustering.

关键词： Energy efciency routing load balancing cluster selection

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Transi-Net: An Explainable Deep Learning Model Ensemble For Prostate's Transition Zone Segmentation 23

Transi-Net: An Explainable Deep Learning Model Ensemble For ...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos S. Kalantzopoulos, Charalampos Marias, Kostas Tsiknakis, Manolis Koutsouris, Dimitrios D. Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical & Computer Engineering Biomedical Research Institute National Technical University of Athens FORTH Athens Ioannina Greece Biomedical Research Institute FORTH Ioannina Greece Computational Biomedicine Laboratory FORTH Heraklion Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Biomedical Research Institute Unit of Medical Technology and Intelligent Information Systems FORTH University of Ioannina Ioannina Greece

ISBN: (纸本)9798350393118

The identification of the location of prostate cancer is of paramount importance for improved treatment. This process is strictly bonded with the accurate segmentation of the prostate gland and its zones, on MR images. In the present study, an ensemble of 3 deep learning models along with a Meta-learner module able to refine the outcomes of the models, is proposed (Transi-Net) to segment the prostate's transition zone. A method to quantify the model's uncertainty is introduced to measure the confidence of an architecture with respect to its final decision. The backbone of Transi-Net consist the original U-net, Dense2U-net and Bridged U-net models. The proposed model showcased significant improvement in comparison with its base components as well as an independent model, the USE-Net, while it was proven more confident about its decision. The proposed model resulted in an improvement of 5%, 3%, 3% and 4% for Sensitivity, Balanced Accuracy, Dice Score and Rand Error Index respectively, compared to the second best, USE-Net. © 2023 IEEE.

关键词： Magnetic resonance imaging

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